No abstract

Developing inexpensive and simple DNA sequencing methods capable of detecting entire genomes in short periods of time could revolutionize the world of medicine and technology. It will also lead to major advances in our understanding of fundamental biological processes. It has been shown that nanopores have the ability of single-molecule sensing of various biological molecules rapidly and at a low cost. This has stimulated significant experimental efforts in developing DNA sequencing techniques by utilizing biological and artificial nanopores. In this review, we discuss recent progress in the nanopore sequencing field with a focus on the nature of nanopores and on sensing mechanisms during the translocation. Current challenges and alternative methods are also discussed.

Selectivity is one of the most fundamental concepts in natural sciences, and it is also critically important in various technological, industrial and medical applications. Although there are many experimental methods that allow to separate molecules, frequently they are expensive and not efficient. Recently, a new method of separation of chemical mixtures based on utilization of channels and nanopores has been proposed and successfully tested in several systems. However, mechanisms of selectivity in the molecular transport during the translocation are still not well understood. Here we develop a simple theoretical approach to explain the origin of selectivity in molecular fluxes through channels. Our method utilizes discrete-state stochastic models that take into account all relevant chemical transitions and can be solved analytically. More specifically, we analyze channels with one and two binding sites employed for separating mixtures of two types of molecules. The effects of the symmetry and the strength of the molecular-pore interactions are examined. It is found that for one-site binding channels the differences in the strength of interactions for two species drive the separation. At the same time, in more realistic two-site systems the symmetry of interaction potential becomes also important. The most efficient separation is predicted when the specific binding site is located near the entrance to the nanopore. In addition, the selectivity is higher for large entrance rates into the the channel. It is also found that the molecular transport is more selective for repulsive interactions than for attractive interactions. The physical-chemical origin of the observed phenomena are discussed.

Background Glioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer. Results We use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma. Conclusions Given the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.